Supplemental Figure 2

Analysis

Dose-response verdinexor

embl2016 <- drugList[["ScreenB"]]

drugSub <- "Verdinexor"

concInter <- embl2016 %>%
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  dplyr::select(conc) %>% unique() %>% unlist() %>% 
  as.vector()
concInter[1:2] <- round(concInter[1:2], 1)
concInter[3:6] <- round(concInter[3:6], 2)
concInter[7:9] <- round(concInter[7:9], 3)

p1 <- embl2016 %>% 
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  ggplot(aes(x = conc, y = viab, group = sampleID)) +
  geom_line(size = 0.25) + 
  geom_point(size = 3.75, shape = 21, fill = "#64B5F6") +
  geom_hline(yintercept = 1, linetype = "dashed") +
  scale_y_continuous(limits = c(0, 1.2)) +
  scale_x_log10(labels = concInter, 
                breaks = concInter) +
  xlab("Concentration (µM)") + ylab("Viability") + 
  theme_classic() + ggtitle(paste("Screen B", drugSub, sep = " - ")) +
  theme(text = element_text(size = 17.5), 
        legend.key = element_blank(),
        plot.title = element_text(hjust=0.5),
        # axis.text.x = element_text(hjust = 1, angle = 45),
        axis.text = element_text(size = 17.5),
        axis.line = element_line(linewidth = 0.75),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent",colour = NA),
        plot.background = element_rect(fill = "transparent",colour = NA))

## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.

p1

ggsave(plot = p1, filename = paste0(opt$plot, drugSub, ".png"), 
       height = 5.25, width = 7)

Dose-response ruxolitinib

drugSub <- "Ruxolitinib"

concInter <- embl2016 %>%
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  dplyr::select(conc) %>% unique() %>% unlist() %>% 
  as.vector()
concInter[1:2] <- round(concInter[1:2], 1)
concInter[3:6] <- round(concInter[3:6], 2)
concInter[7:9] <- round(concInter[7:9], 3)

p1 <- embl2016 %>% 
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  ggplot(aes(x = conc, y = viab, group = sampleID)) +
  geom_line(size = 0.25) + 
  geom_point(size = 3.75, shape = 21, fill = "#64B5F6") +
  geom_hline(yintercept = 1, linetype = "dashed") +
  scale_y_continuous(limits = c(0, 1.2)) +
  scale_x_log10(labels = concInter, 
                breaks = concInter) +
  xlab("Concentration (µM)") + ylab("Viability") + 
  theme_classic() + ggtitle(paste("Screen B", drugSub, sep = " - ")) +
  theme(text = element_text(size = 17.5), 
        legend.key = element_blank(),
        plot.title = element_text(hjust=0.5),
        # axis.text.x = element_text(hjust = 1, angle = 45),
        axis.text = element_text(size = 17.5),
        axis.line = element_line(linewidth = 0.75),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent",colour = NA),
        plot.background = element_rect(fill = "transparent",colour = NA))
p1

ggsave(plot = p1, filename = paste0(opt$plot, drugSub, ".png"), 
       height = 5.25, width = 7)

Dose-response duvelisb

drugSub <- "Duvelisib"

concInter <- embl2016 %>%
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  dplyr::select(conc) %>% unique() %>% unlist() %>% 
  as.vector()
concInter[1:2] <- round(concInter[1:2], 1)
concInter[3:6] <- round(concInter[3:6], 2)
concInter[7:9] <- round(concInter[7:9], 3)

p1 <- embl2016 %>% 
  dplyr::filter(name %in% c(drugSub), 
         diagnosis == "T-PLL") %>%
  ggplot(aes(x = conc, y = viab, group = sampleID)) +
  geom_line(size = 0.25) + 
  geom_point(size = 3.75, shape = 21, fill = "#64B5F6") +
  geom_hline(yintercept = 1, linetype = "dashed") +
  scale_y_continuous(limits = c(0, 1.2)) +
  scale_x_log10(labels = concInter, 
                breaks = concInter) +
  xlab("Concentration (µM)") + ylab("Viability") + 
  theme_classic() + ggtitle(paste("Screen B", drugSub, sep = " - ")) +
  theme(text = element_text(size = 17.5), 
        legend.key = element_blank(),
        plot.title = element_text(hjust=0.5),
        # axis.text.x = element_text(hjust = 1, angle = 45),
        axis.text = element_text(size = 17.5),
        axis.line = element_line(linewidth = 0.75),
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent",colour = NA),
        plot.background = element_rect(fill = "transparent",colour = NA))
p1

ggsave(plot = p1, filename = paste0(opt$plot, drugSub, ".png"), 
       height = 5.25, width = 7)

Show reproducibility

diagList <- c("T-PLL")

screenNames <- c("ScreenB", "ScreenC", "ScreenD", "ScreenA", "ScreenE")

pList <- lapply(diagList, function(x) {
  pList <- lapply(screenNames, function(y) {
    screenData <- drugList[[y]]
    
    screenData <- screenData %>% #filter(diagnosis == x) %>%
      group_by(diagnosis, patientID, name) %>%
      summarise(viab.auc = mean(normVal, na.rm = TRUE)) %>%
      ungroup()
    
    screenNamesOther <- screenNames[which(screenNames != y)]
    
    pList <- lapply(screenNamesOther, function(z) {
      screenDataOther <- drugList[[z]]
    
      screenDataOther <- screenDataOther %>% #filter(diagnosis == x) %>%
        group_by(diagnosis, patientID, name) %>%
        summarise(viab.auc.other = mean(normVal, na.rm = TRUE)) %>%
        ungroup()
      
      ## Intersect the patients
      patOver <- intersect(screenData$patientID, screenDataOther$patientID) %>% unique()
      message("Found ", length(patOver), " overlapping patients")
      
      ## Intersect the drugs
      drugOver <- intersect(screenData$name, screenDataOther$name) %>% unique()
      message("Found ", length(drugOver), " overlapping drugs")
      
      ## Plot overlap
      p <- screenData %>%
        filter(patientID %in% patOver, name %in% drugOver) %>%
        left_join(screenDataOther, by = c("name", "patientID", "diagnosis")) %>%
        ggplot(aes(x = viab.auc, y = viab.auc.other, fill = diagnosis)) +
        geom_point(size = 2, shape = 21) +
        geom_smooth(method = "lm", colour = "black", fill = "grey80") +
        xlab(paste0(gsub(x = y, pattern = "Screen", replacement = "Screen "))) +
        ylab(paste0(gsub(x = z, pattern = "Screen", replacement = "Screen "))) +
        xlim(0, 1.6) + ylim(0, 1.6) +
        #ggtitle(paste0("Drug-Drug corr. ", y, " vs ", z)) +
        lgd +
        theme(legend.position = "none", 
              panel.border = element_rect(linewidth = 1, fill = "transparent")) +
        scale_fill_manual(values = c("MCL" = "#F44336", "T-PLL" = "#64B5F6", "CLL" = "#FFD45F"))
      
      ggsave(plot = p, filename = paste0(opt$plot, "/screen_corr/", x, "_", y, "_", z, ".png"), 
             height = 7, width = 7.5, units = "cm")
      p
    })
    #grid.arrange(grobs = pList, ncol = 1)
    names(pList) <- screenNamesOther
    pList
  }) 
  names(pList) <- screenNames
  pList
})
names(pList) <- diagList

pList

## $`T-PLL`
## $`T-PLL`$ScreenB
## $`T-PLL`$ScreenB$ScreenC

## 
## $`T-PLL`$ScreenB$ScreenD

## 
## $`T-PLL`$ScreenB$ScreenA

## 
## $`T-PLL`$ScreenB$ScreenE

## 
## 
## $`T-PLL`$ScreenC
## $`T-PLL`$ScreenC$ScreenB

## 
## $`T-PLL`$ScreenC$ScreenD

## 
## $`T-PLL`$ScreenC$ScreenA

## 
## $`T-PLL`$ScreenC$ScreenE

## 
## 
## $`T-PLL`$ScreenD
## $`T-PLL`$ScreenD$ScreenB

## 
## $`T-PLL`$ScreenD$ScreenC

## 
## $`T-PLL`$ScreenD$ScreenA

## 
## $`T-PLL`$ScreenD$ScreenE

## 
## 
## $`T-PLL`$ScreenA
## $`T-PLL`$ScreenA$ScreenB

## 
## $`T-PLL`$ScreenA$ScreenC

## 
## $`T-PLL`$ScreenA$ScreenD

## 
## $`T-PLL`$ScreenA$ScreenE

## 
## 
## $`T-PLL`$ScreenE
## $`T-PLL`$ScreenE$ScreenB

## 
## $`T-PLL`$ScreenE$ScreenC

## 
## $`T-PLL`$ScreenE$ScreenD

## 
## $`T-PLL`$ScreenE$ScreenA

screenNames

## [1] "ScreenB" "ScreenC" "ScreenD" "ScreenA" "ScreenE"

## Give R2 and p.adj
cor.df.all <- lapply(diagList, function(x) {
  lapply(screenNames, function(y) {
    screenData <- drugList[[y]]
    
    screenData <- screenData %>% #filter(diagnosis == x) %>%
      group_by(diagnosis, patientID, name) %>%
      summarise(viab.auc = mean(normVal, na.rm = TRUE)) %>%
      ungroup()
    
    screenNamesOther <- screenNames[which(screenNames != y)]
    
    lapply(screenNames, function(z) {
      screenDataOther <- drugList[[z]]
    
      screenDataOther <- screenDataOther %>% #filter(diagnosis == x) %>%
        group_by(diagnosis, patientID, name) %>%
        summarise(viab.auc.other = mean(normVal, na.rm = TRUE)) %>%
        ungroup()
      
      ## Intersect the patients
      patOver <- intersect(screenData$patientID, screenDataOther$patientID) %>% unique()
      message("Found ", length(patOver), " overlapping patients")
      
      ## Intersect the drugs
      drugOver <- intersect(screenData$name, screenDataOther$name) %>% unique()
      message("Found ", length(drugOver), " overlapping drugs")
      
      ## Plot overlap
      cor.df <- screenData %>%
        filter(patientID %in% patOver, name %in% drugOver) %>%
        left_join(screenDataOther, by = c("name", "patientID", "diagnosis")) %>%
        #group_by(name) %>%
        nest() %>%
        mutate(m = map(data, ~cor.test(~viab.auc+viab.auc.other,.))) %>%
        mutate(res = map(m, broom::tidy)) %>%
        unnest(res) %>% ungroup() %>%
        select(estimate, p.value ) %>%
        arrange(p.value)
      cor.df$screen1 <- y
      cor.df$screen2 <- z
      cor.df
    }) %>% bind_rows()
  }) %>% bind_rows()
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p.value, method = "BH"))

kable(cor.df.all %>% arrange(p.adj))

estimate	screen1	screen2
1.0000000	ScreenB	ScreenB
0.7144431	ScreenB	ScreenC
0.6434237	ScreenB	ScreenD
0.7144431	ScreenC	ScreenB
1.0000000	ScreenC	ScreenC
0.7536054	ScreenC	ScreenD
0.6434237	ScreenD	ScreenB
0.7536054	ScreenD	ScreenC
1.0000000	ScreenD	ScreenD
1.0000000	ScreenA	ScreenA
1.0000000	ScreenE	ScreenE
0.5903928	ScreenC	ScreenA
0.5903928	ScreenA	ScreenC
0.6714243	ScreenB	ScreenA
0.6714243	ScreenA	ScreenB
0.8022378	ScreenB	ScreenE
0.8022378	ScreenE	ScreenB
0.8022696	ScreenD	ScreenE
0.8022696	ScreenE	ScreenD
0.6179082	ScreenD	ScreenA
0.6179082	ScreenA	ScreenD
0.8086957	ScreenC	ScreenE
0.8086957	ScreenE	ScreenC
0.6758175	ScreenA	ScreenE
0.6758175	ScreenE	ScreenA

#View(cor.df.all)
max(cor.df.all$p.adj)

## [1] 1.133701e-11

range(cor.df.all[cor.df.all$estimate < 0.99, ]$estimate)

## [1] 0.5903928 0.8086957

## Also per drug
cor.df <- lapply(diagList, function(x) {
  lapply(screenNames, function(y) {
    screenData <- drugList[[y]]
    
    screenData <- screenData %>% #filter(diagnosis == x) %>%
      group_by(diagnosis, patientID, name) %>%
      summarise(viab.auc = mean(normVal, na.rm = TRUE)) %>%
      ungroup()
    
    screenNamesOther <- screenNames[which(screenNames != y)]
    
    lapply(screenNamesOther, function(z) {
      screenDataOther <- drugList[[z]]
    
      screenDataOther <- screenDataOther %>% #filter(diagnosis == x) %>%
        group_by(diagnosis, patientID, name) %>%
        summarise(viab.auc.other = mean(normVal, na.rm = TRUE)) %>%
        ungroup()
      
      ## Intersect the patients
      patOver <- intersect(screenData$patientID, screenDataOther$patientID) %>% unique()
      message("Found ", length(patOver), " overlapping patients")
      
      ## Intersect the drugs
      drugOver <- intersect(screenData$name, screenDataOther$name) %>% unique()
      message("Found ", length(drugOver), " overlapping drugs")
      
      ## Plot overlap
      cor.df <- screenData %>%
        filter(patientID %in% patOver, name %in% drugOver) %>%
        left_join(screenDataOther, by = c("name", "patientID", "diagnosis")) %>%
        group_by(name) %>%
        nest() %>%
        mutate(m = map(data, ~cor.test(~viab.auc+viab.auc.other,.))) %>%
        mutate(res = map(m, broom::tidy)) %>%
        unnest(res) %>% ungroup() %>%
        select(name, estimate, p.value ) %>%
        arrange(p.value) 
      cor.df$screen1 <- y
      cor.df$screen2 <- z
      
      cor.df
    }) %>% bind_rows()
  }) %>% bind_rows()
}) %>% bind_rows() %>% mutate(p.adj = p.adjust(p.value, method = "BH"))
cor.df %>% filter(name %in% c("Birinapant", "Selinexor"))

## # A tibble: 8 × 6
##   name       estimate   p.value screen1 screen2    p.adj
##   <chr>         <dbl>     <dbl> <chr>   <chr>      <dbl>
## 1 Selinexor     0.338 0.000174  ScreenB ScreenD 0.000671
## 2 Birinapant    0.755 0.000120  ScreenB ScreenE 0.000514
## 3 Selinexor     0.306 0.309     ScreenB ScreenE 0.399   
## 4 Selinexor     0.338 0.000174  ScreenD ScreenB 0.000671
## 5 Selinexor     0.912 0.0000355 ScreenD ScreenE 0.000177
## 6 Birinapant    0.755 0.000120  ScreenE ScreenB 0.000514
## 7 Selinexor     0.306 0.309     ScreenE ScreenB 0.399   
## 8 Selinexor     0.912 0.0000355 ScreenE ScreenD 0.000177

Output session info

## R version 4.3.2 (2023-10-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## time zone: Etc/UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] parallel  grid      stats     graphics  grDevices datasets  utils    
## [8] methods   base     
## 
## other attached packages:
##  [1] knitr_1.48               gridExtra_2.3            ggrepel_0.9.5           
##  [4] ComplexHeatmap_2.18.0    DrugScreenExplorer_0.1.0 jyluMisc_0.1.5          
##  [7] lubridate_1.9.3          forcats_1.0.0            stringr_1.5.1           
## [10] dplyr_1.1.4              purrr_1.0.2              readr_2.1.5             
## [13] tidyr_1.3.1              tibble_3.2.1             ggplot2_3.5.1           
## [16] tidyverse_2.0.0          readxl_1.4.3            
## 
## loaded via a namespace (and not attached):
##   [1] splines_4.3.2               later_1.3.2                
##   [3] bitops_1.0-7                cellranger_1.1.0           
##   [5] lifecycle_1.0.4             Rdpack_2.6                 
##   [7] rstatix_0.7.2               doParallel_1.0.17          
##   [9] lattice_0.21-9              MASS_7.3-60                
##  [11] backports_1.5.0             magrittr_2.0.3             
##  [13] limma_3.58.1                sass_0.4.9                 
##  [15] rmarkdown_2.27              jquerylib_0.1.4            
##  [17] yaml_2.3.9                  plotrix_3.8-4              
##  [19] httpuv_1.6.15               cowplot_1.1.3              
##  [21] RColorBrewer_1.1-3          multcomp_1.4-25            
##  [23] abind_1.4-5                 zlibbioc_1.48.2            
##  [25] GenomicRanges_1.54.1        BiocGenerics_0.48.1        
##  [27] RCurl_1.98-1.14             TH.data_1.1-2              
##  [29] sandwich_3.1-0              circlize_0.4.16            
##  [31] GenomeInfoDbData_1.2.11     IRanges_2.36.0             
##  [33] KMsurv_0.1-5                S4Vectors_0.40.2           
##  [35] codetools_0.2-19            DelayedArray_0.28.0        
##  [37] DT_0.33                     tidyselect_1.2.1           
##  [39] shape_1.4.6.1               farver_2.1.2               
##  [41] matrixStats_1.3.0           stats4_4.3.2               
##  [43] jsonlite_1.8.8              GetoptLong_1.0.5           
##  [45] dr4pl_2.0.0                 survival_3.5-7             
##  [47] iterators_1.0.14            systemfonts_1.1.0          
##  [49] foreach_1.5.2               tools_4.3.2                
##  [51] ragg_1.3.2                  Rcpp_1.0.12                
##  [53] glue_1.7.0                  SparseArray_1.2.4          
##  [55] xfun_0.45                   mgcv_1.9-0                 
##  [57] MatrixGenerics_1.14.0       piano_2.18.0               
##  [59] GenomeInfoDb_1.38.8         shinydashboard_0.7.2       
##  [61] withr_3.0.0                 fastmap_1.2.0              
##  [63] fansi_1.0.6                 shinyjs_2.1.0              
##  [65] relations_0.6-13            caTools_1.18.2             
##  [67] digest_0.6.36               timechange_0.3.0           
##  [69] R6_2.5.1                    mime_0.12                  
##  [71] textshaping_0.4.0           colorspace_2.1-0           
##  [73] gtools_3.9.5                tensor_1.5                 
##  [75] utf8_1.2.4                  generics_0.1.3             
##  [77] renv_1.0.7                  data.table_1.15.4          
##  [79] htmlwidgets_1.6.4           S4Arrays_1.2.1             
##  [81] sets_1.0-25                 pkgconfig_2.0.3            
##  [83] gtable_0.3.5                XVector_0.42.0             
##  [85] survMisc_0.5.6              htmltools_0.5.8.1          
##  [87] carData_3.0-5               fgsea_1.28.0               
##  [89] clue_0.3-65                 scales_1.3.0               
##  [91] Biobase_2.62.0              png_0.1-8                  
##  [93] km.ci_0.5-6                 rstudioapi_0.16.0          
##  [95] tzdb_0.4.0                  rjson_0.2.21               
##  [97] nlme_3.1-163                visNetwork_2.1.2           
##  [99] cachem_1.1.0                zoo_1.8-12                 
## [101] GlobalOptions_0.1.2         KernSmooth_2.23-22         
## [103] pillar_1.9.0                vctrs_0.6.5                
## [105] maxstat_0.7-25              gplots_3.1.3.1             
## [107] slam_0.1-50                 promises_1.3.0             
## [109] ggpubr_0.6.0                car_3.1-2                  
## [111] xtable_1.8-4                cluster_2.1.4              
## [113] evaluate_0.24.0             mvtnorm_1.2-5              
## [115] cli_3.6.3                   compiler_4.3.2             
## [117] rlang_1.1.4                 crayon_1.5.3               
## [119] ggsignif_0.6.4              survminer_0.4.9            
## [121] labeling_0.4.3              marray_1.80.0              
## [123] stringi_1.8.4               BiocParallel_1.36.0        
## [125] munsell_0.5.1               Matrix_1.6-1.1             
## [127] hms_1.1.3                   statmod_1.5.0              
## [129] shiny_1.8.1.1               SummarizedExperiment_1.32.0
## [131] highr_0.11                  rbibutils_2.2.16           
## [133] drc_3.0-1                   exactRankTests_0.8-35      
## [135] igraph_2.0.3                broom_1.0.6                
## [137] bslib_0.7.0                 fastmatch_1.1-4

Supplemental Figure 2

M. Pohly

2024-11-25

Load libraries

Define variables

Load drug screen data and annotations

Analysis

Dose-response verdinexor

Dose-response ruxolitinib

Dose-response duvelisb

Show reproducibility

Output session info